FIG. 10 shows a block diagram of conventional signal quality measuring schemes described in ["A Scheme for High Performance Real-Time BER Measurement", IEEE Trans. On Commun., Vol. 40, No. 10, October 1992 pp. 1574-1576].
In FIG. 10, the conventional signal quality measuring scheme consists of a signal detecting unit 101 for detecting and demodulating a received signal which is phase modulated, and outputting the signal as a signal point with phase information at a decision point such as a Nyquist point. The scheme further consists of, a phase region deciding section 102 for deciding to which phase region, of a plurality of different preset phase regions, the signal point outputted from the signal detecting section 101, namely the demodulated phase information corresponds. A counter section 103 counts a number of signal points in each phase region. A phase-distribution table storing section 106 stores a phase-distribution table representing a relation between a phase distribution probability in each phase region and a signal quality obtained by actual measurement or simulation or theoretical computation or the like. A likelihood calculating section 107 is provided for calculating a likelihood of phase distribution of each phase-distribution model obtained according to the count in the counter section 103, then detecting a phase distribution of the maximum likelihood phase-distribution model and outputting an estimated signal quality corresponding to that detected phase-distribution model.
The counter section 103 consists of a plurality of counter sections in region 1 to region M, 103-1, 103-2, . . . 103-M for counting the signal points outputted from the signal detecting section 101 for each of a plurality of different preset phase regions. Wherein M indicates a number of phase regions.
FIG. 11A shows a diagram representing a decision region for the phase distribution when a QPSK-modulated signal is received in the signal detecting section 101. In FIG. 11A, the upper right quadrant representing decision regions in phase distribution divided into four phase regions each having the same width assuming that a phase region closest to a phase .pi./4 is R1 of phases (.pi./4, 3.pi./4, -.pi./4, -3.pi./4) demodulated in the signal detecting section 101 and that phase regions adjacent to the phase region R.sub.1 are R.sub.2, R.sub.3, R.sub.4 in this order. It should be noted that the phases 3.pi./4, -.pi./4, -3.pi./4 can be represented in the upper left quadrant, in the lower left quadrant, and in the lower right quadrant respectively in the same manner as described above.
When a signal point, which is transmitted with the phase information .pi./4 and outputted from the signal detecting section 101, arrives through an ideal channel, then it is considered as S.sub.1. This signal point is positioned on the central line of the phase region R.sub.1, namely on the line indicating the phase .pi./4 in the upper right quadrant as shown in FIG. 11A. However, when there is a thermal noise or fading in the channel, the signal point which should originally indicate the phase information .pi./4 is displaced from the line indicating the phase .pi./4 like the point S.sub.2. Especially, when the phase in this case is displaced by .pi./4 or more from the original signal point, then the signal point gets positioned in a different quadrant which results in a bit error.
Namely, in FIG. 11A, it is clear that a bit error rate (BER) and a signal-to-noise power ratio (noise power corresponding to a power of a 1-bit information signal: Eb/No) are high in order of the phase regions R.sub.1, R.sub.2, R.sub.3, and R.sub.4. By measuring a probability of a signal point falling in these phase regions (called "phase distribution probability" hereinafter), the bit error rate and signal-to-noise power ratio can be estimated.
The signal point outputted from the signal detecting section 101 can be obtained, for example, in the following manner. First, a relation between each phase distribution probability and a bit error rate or a signal-to-noise power ratio is measured, and then the bit error rate or the signal-to-noise power ratio of successively received signals is detected using a table indicating the relation. Any of these numerical values (estimated value) can be regarded as an estimated signal quality.
In the case described above, the counter section 103 has signal counter sections 103-1, 103-2, 103-3, and 103-4 each for counting a number of signal points falling in each phase regions R.sub.1, R.sub.2, R.sub.3, and R.sub.4 (namely, it is assumed that M is equal to four). As shown in FIG. 11A, when a signal point S.sub.1 originally transmitted as a transmission signal but received as a signal point S.sub.2 in the signal detecting section 101, it is decided by the phase region deciding section 102 that the signal point S.sub.2 is in the phase region R.sub.2, and the point is counted in the counter section in region 2, 103-2 corresponding to the phase region R.sub.2.
As described above, when phase regions into which the signals received by the signal detecting section 101 in a specified measurement period fall is decided, and when number of points in each phase regions is counted, the phase distribution as shown in FIG. 11 can be obtained. The above mentioned phase distribution probability can be derived from this phase distribution.
Herein, it may be considered that a caller (a person who makes the communication) who is moving makes the communication through a fading channel while a caller who is standing still makes the communication through a Gaussian channel. The relation between the phase distribution probability and a bit error rate or the signal-to-noise power ratio is generally different depending on the channel condition during the communications. Thus, there may occur a problem that accurate bit error rate or accurate signal-to-noise power ratio can not be estimated simply by measuring the phase distribution probability.
In order to solve this problem, a phase-distribution table representing a relation between the phase distribution probability and the signal quality (such as the bit error rate or the signal-to-noise power ratio) is prepared for some of the typical channel models such as the fading channel or the Gaussian channel. This phase-distribution table is stored in the phase-distribution table storing section 106.
In practice, the phase-distribution table is a table that represents a relation between a phase distribution probability of each phase regions and the signal quality obtained by actual measurement or simulation or theoretical computation or the like.
The likelihood calculating section 107 refers to this phase-distribution table stored in the phase-distribution table storing section 106. Then the likelihood calculating section 107 calculates each likelihood of the phase distribution obtained from a result of counting by the counter section 103 corresponding to each phase-distribution model, detects a phase-distribution model whose posteriori probability is the maximum, namely the maximum likelihood phase-distribution model, and outputs an estimated signal quality corresponding to the detected phase-distribution model. In this case, the likelihood L (k) of the phase-distribution model k is obtained through the equation described below. ##EQU1##
Where count corresponding to four phase regions are u.sub.1 to u.sub.4 respectively, each logarithm of values corresponding to the phase regions of the phase-distribution model k in the phase-distribution table are v.sub.1k to v.sub.4k, and the logarithm of normal probability of the phase-distribution model k is .lambda..sub.k. However, the conventional signal quality measuring scheme estimates the signal quality by marking off a certain period of time having a continuous data, so that precision in estimation of the signal quality is decided by the duration of the time period (measurement period). Therefore, in order to obtain a sufficiently high precision in a low signal-to-noise power ratio, the measurement period is required to be taken sufficiently long. When the scheme is applied to an actual system, however, it is conceivable that a channel condition with time or data is received with bursts like TDMA (time division multiple access) communications. When the channel varies with time, and if a measurement period is too long, a response to time variation of the channel becomes worse, therefore precision in the estimation can not be enhanced. In case of data transaction with bursts like TDMA communications, when the signal quality is measured in the units of bursts, and if a burst size is small, precision in the estimation of the signal quality is reduced.